Upper Confidence Bound

Upper Confidence Bound (UCB) is a statistical method used in machine learning and artificial intelligence to balance exploration and exploitation in multi-armed bandit problems. In a multi-armed bandit problem, an agent must decide which action to take at each step to maximize the cumulative reward. UCB algorithm selects the action with the highest upper confidence bound, which is a measure of the uncertainty of the estimated reward for each action. The algorithm balances exploration by selecting actions with high uncertainty and exploitation by selecting actions with high estimated reward. UCB algorithm is widely used in online advertising, recommendation systems, and clinical trials.
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